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Dataset . 2021
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ZENODO
Dataset . 2021
License: CC BY
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Telecommunication Networks as Knowledge Graph Research Datasets

Authors: Vodyaho, Alexander; Ignatov, Dmitry; Kulikov, Igor; Zhukova, Nataly; Tianxing, Man;

Telecommunication Networks as Knowledge Graph Research Datasets

Abstract

Telecommunication Networks as Knowledge Graph Research Datasets. All the datasets were created using the PC with following parameters: Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz/16.00 GB RAM 1TB SSD All the software which is used for the datasets creation is available here: https://github.com/kulikovia/TN_KG_research The datasets and supplementary files description of follow: # File Description 1. Computational_complexity_v5_ (ENG).pdf SPARQL performance tests report 2. Synthesis_performance_tests_results_v1.pdf Inductive and deductive synthesis performance tests report 3. Computational_complexity_Parallel_v2_ (ENG).pdf Comparision of SPARQL performanse using multi-level KG structure approach and execution using distributed RDF storege 4. Synthesis_additional_experiments_v4.pdf Additional Inductive and deductive synthesis performance tests report (with different elements distribution by levels) 5. Dataset_10k_hierarchical.zip Inductive and deductive synthesis performance tests: Input data for 10k hierarchical model synthesis (CSV) 6. Dataset_10k_one-level.zip Inductive and deductive synthesis performance tests: Input data for 10k one-level model synthesis (CSV) 7. Dataset_1k_hierarchical.zip Inductive and deductive synthesis performance tests: Input data for 1k hierarchical model synthesis (CSV) 8. Dataset_1k_one-level.zip Inductive and deductive synthesis performance tests: Input data for 1k one-level model synthesis (CSV) 9. Dataset_200k_hierarchical.zip Inductive and deductive synthesis performance tests: Input data for 200k hierarchical model synthesis (CSV) 10. Dataset_200k_one-level.zip Inductive and deductive synthesis performance tests: Input data for 200k one-level model synthesis (CSV) 11. Dataset_500k_hierarchical.zip Inductive and deductive synthesis performance tests: Input data for 500k hierarchical model synthesis (CSV) 12. Dataset_500k_one-level.zip Inductive and deductive synthesis performance tests: Input data for 500k one-level model synthesis (CSV) 13. Synthesis_Hierarchical_RDF-XML.zip Inductive and deductive synthesis performance tests: RDF/XML datasets for hierarchical models (1k, 10k, 200k, 500k) 14. Synthesis_Linear_RDF-XML.zip Inductive and deductive synthesis performance tests: RDF/XML datasets for hierarchical models (1k, 10k, 200k, 500k) 15. Hierarchy_model_results_Exp_10M.zip SPARQL performance tests: Datasets for 10M, 3-5-levels, exponential distributed model with connections between source models on levels 2 and 3 in RDF/XML format 16. Hierarchy_model_results_Exp_15M.zip SPARQL performance tests: Datasets for 15M, 3-5-levels, exponential distributed model with connections between source models on levels 2 and 3 in RDF/XML format 17. Hierarchy_model_results_Exp_200k.zip SPARQL performance tests: Datasets for 200k, 3-5-levels, exponential distributed model with connections between source models on levels 2 and 3 in RDF/XML format 18. Hierarchy_model_results_Linear_10M.zip SPARQL performance tests: Datasets for 10M, 3-5-levels, linear distributed model with connections between source models on levels 2 and 3 in RDF/XML format 19. Hierarchy_model_results_Linear_15M.zip SPARQL performance tests: Datasets for 15M, 3-5-levels, linear distributed model with connections between source models on levels 2 and 3 in RDF/XML format 20. Hierarchy_model_results_Linear_200k.zip SPARQL performance tests: Datasets for 200k, 3-5-levels, linear distributed model with connections between source models on levels 2 and 3 in RDF/XML format 21. Hierarchy_model_results_Quadro_10M.zip SPARQL performance tests: Datasets for 10M, 3-5-levels, quadratic distributed model with connections between source models on levels 2 and 3 in RDF/XML format 22. Hierarchy_model_results_Quadro_15M.zip SPARQL performance tests: Datasets for 15M, 3-5-levels, quadratic distributed model with connections between source models on levels 2 and 3 in RDF/XML format 23. Hierarchy_model_results_Quadro_200k.zip SPARQL performance tests: Datasets for 200k, 3-5-levels, quadratic distributed model with connections between source models on levels 2 and 3 in RDF/XML format 24. Hierarchy_model_results_Uniform_10M.zip SPARQL performance tests: Datasets for 10M, 3-5-levels, uniform distributed model with connections between source models on levels 2 and 3 in RDF/XML format 25. Hierarchy_model_results_Uniform_15M.zip SPARQL performance tests: Datasets for 15M, 3-5-levels, uniform distributed model with connections between source models on levels 2 and 3 in RDF/XML format 26. Hierarchy_model_results_Uniform_200k.zip SPARQL performance tests: Datasets for 200k, 3-5-levels, uniform distributed model with connections between source models on levels 2 and 3 in RDF/XML format 27. Linear_model_resuts.zip SPARQL performance tests: Datasets for 200k, 10M, 15M, one-level model in RDF/XML format

Keywords

Synthesis Method, Telecommunication Network, Knowledge Graphs

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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